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Dimension reduction for functional regression with a binary response

Author

Listed:
  • Guochang Wang

    (Jinan University)

  • Beiting Liang

    (Jinan University)

  • Hansheng Wang

    (Peking University)

  • Baoxue Zhang

    (University of Economics and Business)

  • Baojian Xie

    (Jinan University)

Abstract

We propose here a novel functional inverse regression method (i.e., functional surrogate assisted slicing) for functional data with binary responses. Previously developed method (e.g., functional sliced inverse regression) can detect no more than one direction in the functional sufficient dimension reduction subspace. In contrast, the proposed new method can detect multiple directions. The population properties of the proposed method is established. Furthermore, we propose a new method to estimate the functional central space which do not need the inverse of the covariance operator. To practically determine the structure dimension of the functional sufficient dimension reduction subspace, a modified Bayesian information criterion method is proposed. Numerical studies based on both simulated and real data sets are presented.

Suggested Citation

  • Guochang Wang & Beiting Liang & Hansheng Wang & Baoxue Zhang & Baojian Xie, 2021. "Dimension reduction for functional regression with a binary response," Statistical Papers, Springer, vol. 62(1), pages 193-208, February.
  • Handle: RePEc:spr:stpapr:v:62:y:2021:i:1:d:10.1007_s00362-019-01083-1
    DOI: 10.1007/s00362-019-01083-1
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    References listed on IDEAS

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    1. R. Dennis Cook & Bing Li & Francesca Chiaromonte, 2007. "Dimension reduction in regression without matrix inversion," Biometrika, Biometrika Trust, vol. 94(3), pages 569-584.
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    3. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
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    6. Guochang Wang & Jianjun Zhou & Wuqing Wu & Min Chen, 2017. "Robust functional sliced inverse regression," Statistical Papers, Springer, vol. 58(1), pages 227-245, March.
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